You Can't Steer a Parked Car

Fans often ask my wife, pro equestrian Elisa Wallace, if she still gets nervous. Her answer is always: yes.

Even at the highest levels of equestrian competition, it is not uncommon for athletes to involuntarily evacuate the contents of their stomach before an event. With pressure coming from large numbers of spectators (in person and on television), the hopes and dreams of fans and country, and — most importantly — the internal desire to do justice to the potential of her equine partner, a spike in adrenaline is impossible to avoid.

That involuntary physiological response is completely natural. It is a function of the fact that Elisa cares. If it didn’t happen, something would be wrong.

I’ve been thinking a lot about this this adrenaline response in my own professional life. Although still relatively new to speaking in front of very large audiences, I’ve been public speaking now for a long time, both as a teacher and as a scholar. It seems that no matter how much experience I get, I can’t overcome the experience of an involuntary adrenaline response prior to taking a stage.

This is something that I worry about, since I know that this response has an impact on my ability to think clearly, and to recall even the most well-practiced talk tracks. I worry about a quivering voice. I worry about fumbling about on stage, dropping things, and losing my train of thought.

Recently, I asked my wife how she deals with this kind of pre- performance stress response. She gave me three pieces of advice, based on her own experience as a professional athlete:

1. Embrace it.

The only reason you experience an adrenaline response prior to engaging in a public activity (or any activity for that matter) is that you care. That’s a good thing. The worst thing you can do is to stress out about stressing out. Instead, expect your adrenaline to spike and embrace it as an important part of your process. By simply reinterpreting this physiological response as working for you instead of against you, you can transform a hindrance into a helper.

2. You deserve to be there.

A lot of our anxiety comes from insecurity. For anyone with a realistic self-concept, it can be difficult to overcome ‘impostor syndrome.’ Whether you are a professional athlete or a public speaker, remember that you have worked hard, and the only reason you are there is because others want to see you there. You are there because you are already respected, and others already value your opinion. You have nothing to prove. Just do what you came to do.

3. Get pumped.

(1) and (2) are about mindset. This point is about how to get there. Many professional athletes have mastered the art of creating portable fortresses of solitude. They put their headphones on, listen to music, and tune out. Elisa has a ‘pump up’ playlist on her phone. Prior to going on cross country (the most thrilling and dangerous of her three phases), listening to music is helpful in two ways. It simultaneously (and paradoxically) helps you to tune out extraneous information so you can focus on the task at hand, and distracts you (in a productive way) from the importance of what you are about to do. Preference, of course, is music with driving bass lines, which we know from research has the effect of boosting confidence as well.

In March 2017, Manuela Ekowo and Iris Palmer co-authored a report for New America that offered five guiding practices for the ethical use of predictive analytics in higher education. This kind of work is really important. It acknowledges that, to the extent that analytics in higher education is meant to have an impact on human behavior, it it is a fundamentally ethical enterprise.

Work like the recent New America report is not merely about educational data science. It is an important facet of educational data science itself.

Are we doing ethics well?

But ethics is hard. Ethics is not about generating a list of commandments. It is not about cataloging common opinion. It is about carefully establishing a set of principles on the basis of which it becomes possible to create a coherent system of knowledge and make consistent judgements in specific situations.

Unfortunately, most work on the ethics of analytics in higher education lacks this kind of rigor. Instead, ethical frameworks are the result of a process of pooling opinions in such a way as to strike a balance between the needs of a large number of stakeholders including students, institutions, the economy, the law, and public opinion. To call this approach ethics is to confuse the good with the expedient.

Where should we begin?

An ethical system worthy of the name needs to begin with a strong conception of the Good. Whether stated or implied, the most common paradigm is essentially utilitarian, concerned with maximizing benefit for the greatest number of people. The problem with this approach, however, is that it can only ever concern perceived benefit. People are famously bad at knowing what is good for them.

A benefit of this utilitarian approach, of course, is that it allows us to avoid huge epistemological and metaphysical minefields. In the absence of true knowledge of the good, we can lean on the wisdom of crowds. By pooling information about perceived utility, so the theory goes, we can approximate something like the good, or at least achieve enough consensus to mitigate conflict as much as possible.

But what if we were more audacious? What if our starting point was not the pragmatic desire to reduce conflict, but rather an interest in fostering the fullest expression of our potential as humans? As it specifically pertains to the domain of educational data analytics, what if we abandoned that instrumental view of student success as degree completion? What if we began with the question of what it means to be human, and wrestled with the ways in which the role of ‘student’ is compatible and incompatible with that humanity?

Humane data ethics in action

Let’s consider one example of how taking human nature seriously affects how we think about analytics technologies. As the Italian humanist Pier Paolo Vergerio observed, all education is auto-didactic. When we think about teaching and learning, the teacher has zero ability to confer knowledge. It is always the learner’s task to acquire it. True, it is possible to train humans just as we can train all manner of other creatures (operant and classical forms of conditioning are incredibly powerful). but this is not education. Education is a uniquely human capability whereby we acquire knowledge (with the aim of living life in accord with the Good). Teachers do not educate. Teachers do not ‘teach.’ Rather, it is the goal of the teacher to establish the context in which the student might become actively engaged as learners.

What does this mean for Education? Viewed from this perspective, it is incumbent on us as educators to create contexts that bring students to an awareness of themselves as learners in the fullest sense of the word. It is crucial that we develop technologies that highlight the student’s role as autodidact. Our technologies need to help bring students to self-knowledge at the same time as they create robust contexts for knowledge acquisition (in addition to providing opportunities for exploration, discovery, experimentation, imagination and other humane attributes).

It is in large part this humanistic perspective that has informed my excitement about student-facing dashboards. As folks like John Fritz have talked about, one of the great things about putting data in the hands of students is that it furthers institutional goals like graduation and retention as a function of promoting personal responsibility and self-regulated learning. In other words, by using analytics first and foremost with an interest in helping students to understand and embrace themselves as learners in the fullest sense of the term, we cultivate virtues that translate into degree completion, but also career success and life satisfaction.

In my opinion, analytics (predictive or otherwise) are most powerful when employed with a view to maximizing self-knowledge and the fullest expression of human capability rather than as way to constrain human behavior to achieve institutional goals. I am confident that such a virtuous and humanistic approach to educational data analytics will also lead to institutional gains (as indeed we have seen at places like Georgia State University), but worry that where values and technologies are not aligned, both human nature and institutional outcomes are bound to suffer.

My father is leaving his working life as I feel that mine is getting started. It seems fitting, then, to use may father’s retirement as an occasion to look back at the lessons he has taught me over the years, and that continue to shape how I approach business and life. There are many. Here are three.

Don Harfield

You can’t steer a parked car.

You can’t steer a parked car. This is great advice for surviving and thriving amidst conditions of uncertainty. None of us know what the future holds. Increasingly, we need to expect the unexpected. Rather than be paralyzed in the face of the unknown, what I have learned from my father is the importance of passionately pursuing a goal, committing yourself to a particular direction, while also being flexible and open to changing trajectory (sometimes radically) as conditions change. As my father retires, his advice continues to be relevant regardless of your stage in career and in life.

Don’t be risk averse. Be risk aware.

Being risk averse produces fear, and leads to an inability to act. Being afraid of risk leads to decisions that are as bad as if risk is unacknowledged. What risk aversion and its opposite have in common is a kind of laziness. If you don’t understand a project and the factors that condition its success, then you are stuck with temperament, simple heuristics, and ‘intuition.’ It is important to put in the work necessary to understand potential risks as much as possible, establish mechanisms to mitigate those risks, and build contingency into any plan to account for risks that you may not have identified or fully appreciated.

Do the right thing. Put people first.

In many ways, I feel like my belief in the importance of virtue can be traced back to the model my father set for me. Do the right thing. Put people first. Have faith that, in doing what’s right, success will happen as a matter of course. An important part of this is to avoid overdetermining what success looks like. It might mean fame of fortune, but it might also mean forming important relationships, achieving a sense of peace, or leaving an indelible mark on your community. If you go about your life chasing after success, whatever it is you’ll always miss the mark. If, on the other hand, you seek only after what is good, you’ll achieve success every time.

Sometimes the most effective way of communicating the right way to do something is by highlighting the consequences of doing the opposite. It’s how sitcoms work. By creating humorous situations that highlight the consequences of breeching social norms, those same norms are reinforced.

At the 2017 Blackboard Analytics Symposium, A. Michael Berman, ‎VP for Technology & Innovation at CSU Channel Islands and Chief Innovation Officer for California State University, harnessed his inner George Costanza to deliver an ironic, hilarious, and informative talk about strategies for failing with data.

What does this self-proclaimed ‘Tony Robbins of project failure’ suggest?

Set unclear goals – setting unclear goals takes a lot of hard work and may require compromise. It’s way more democratic to let everyone set their own goals. That way, everyone can have their own criteria for success, which guarantees that whatever you do almost everyone is going to think of you as a failure.

Avoid Executive Support – Going out and getting executive support is also a lot of work. It means going to busy executives, getting time of their calendar, and speaking to them in terms they understand. It also means taking the time to listen and understand what is important to them. Why not go it alone? Sure, it’s unlikely that you will achieve very much, but it’ll be a whole lot of fun.

Emphasize the Tech – Make the project all about technology. And make sure to use as many acronyms as possible. Larger outcomes don’t matter. They are not your problem. Focus on what you do best: processing the data and making sure it flows through your institution’s systems.

Minimize Communication – Why even bother to make people’s eyes glaze over when talking about technology when you can avoid talking to anyone at all? Instead of having a poor communication strategy, it’s better to have no communication strategy at all. You’ll save the time and inconvenience of dealing with people questioning what you do, because they won’t know what you’re doing.

Don’tCelebrate Success – If you have done everything to fail, but still succeed despite yourself, it’s very important not to celebrate. Why bother having a party when people are already getting paid? Why take time out of the work day to reward people for doing their jobs? Isn’t it smarter to just tell everyone to get back to work? Seems like a far more efficient use of institutional resources.

Speaking from personal experience, Michael Berman insists that following these five strategies will virtually guarantee that you drive your data project into the ground. If failing isn’t your thing, and you’d rather succeed in your analytics projects, do the opposite of these five things and you should be just fine.

For the last month, I have been tracking the terms “Product Marketing” and “Product Marketer” using Google alerts. In that time, except for a few exceptions, all I have see are job advertisements. A LOT of job advertisements. For a position that is in such high demand, the fact that there is so little written about it is remarkable indeed.

So, what is product marketing? It’s complicated.

It is commonly accepted that product marketing exists at the intersection of marketing, product management, and sales. A product marketer ‘owns’ messaging for a product or product line. In support of field and central marketing, they work to ensure that what a product ‘means’ is coherent, consistent with broader corporate messaging and brand standards, and compelling to a full range of buying personas. The messaging produced by a product marketer comes to life in two forms: through outward-facing collateral used for demand generation, and inward-facing resources used for sales enablement.

So what is a product marketer? They are a story-teller who serves the interests of marketing, product, and sales through the creation of messaging that is coherent, consistent, and compelling.

It would be easy to stop here and think of the product marketer as a person in the present, as someone who creates stories that strike a balance between the three types of organizational interest it serves. Is a product marketer someone who creates messages that ‘work’ here and now? Yes. But if we also take seriously the role of a product marketer in creating, not just meaning, but also vision, then the product marketer also bears a kind of responsibility to the future. And as it turns out, the most effective and impactful product narratives are those that point beyond an immediate need and toward a future in which a thing is not only useful, but also important.

For me, the most exciting part of product marketing is its relationship to product management. This relationship is not one-way. It is not as if product management creates a thing, and then hands it to ‘the marketing guy’ to ‘market.’ To the extent that a product marketer is responsible for what a thing means, they also have a direct impact on what it becomes. With a meaning that is coherent, consistent, and compelling comes an understanding of the problems and needs of the market. It also necessarily defines values. By working with product management to understand, not just what is possible, but also what is meaningful, the product marketer importantly contributes to a vision for a product that is actualized in the form of a roadmap.

If you can’t say something important, don’t say anything at all.

How common is the commitment to importance among product marketers? I can’t say. But I would like to think that a commitment to importance is essential to being an excellent product marketer. It renders the role itself important (as opposed to merely useful). But with importance comes greater responsibility. It means developing domain expertise over and above the general expertise of being a product marketer. With domain expertise comes a greater sense of empathy for the industries your product supports.

The minute that a product marketer shifts their perspective from the present to the future, their locus of responsibility also changes. Focused on the present, the product marketer is an advocate on behalf of the product to the market. Focused on the future, the product marketer serves as an advocate to the product on behalf of the market.

What, then, is a product marketer? They are a story-teller who advocates on behalf of the market to an organization’s marketing, product, and sales departments through the creation of narratives that are coherent, consistent, and compelling.

In response to the 2017 NMC Horizon report, Mike Sharkey recently observed that analytics had disappeared from the educational technology landscape. After being on the horizon for many years, it seems to have vanished from the report without pomp or lamentation.

For those of us tracking the state of analytics according to the New Media Consortium, we have eagerly awaited analytics’ arrival. In 2011, the time to wide-scale adoption was expected to be four to five years. In 2016, time to adoption was a year or less. In 2017, I would have expected one of two things from the Horizon Report: either (a) great celebration as the age of analytics had finally arrived, or (b) acknowledgment that analytics had not arrived on time.

But we saw neither.

Upon first inspection, analytics seems to have vanished into thin air. But, as Sharkey observes, this was not actually the case. Instead, analytics’ absence from the report was itself a kind of acknowledgement that analytics is not actually ‘a thing’ that can be bought and sold. It is not something that can be ‘adopted.’ Instead, analytics is simply an approach that can be taken in response to particular institutional problems. In other words, to call out analytics as ‘a thing,’ is to establish a solution in search of a problem, as if ‘not having analytics’ was a problem itself that needed to be solved. Analytics never arrived because it was never on its way. The absence of analytics from the horizon report, then, points to the fact that we now understand analytics far better than we did in 2011. If we knew then what we know now, analytics would not have been featured in the horizon report in the first place. We would have put understanding ahead of tools, and bypassed the kind of hype out of which we are only now beginning to emerge.

I agree with Mike. But I want to go a step further. I have always been fascinated by ontologies, and the ways in which the assumptions we make about ‘thingness’ affect our behavior. I have a book in press about the emergence of the modern conception of society. I have written about love (Is it a thing? Is it an activity? Is it a relation? Is it something else?). And I have written about dirt. Mike’s post has served as a catalyst for the convergence of some of my thinking about analytics and ‘thingness.’

Analytics is not a thing. I can produce a dashboard, but I can’t point to that dashboard and say “there is analytics.” There is a important sense in which analytics involves the rhetorical act of translating information in such a way as to render it meaningful. In this, a dashboard only becomes ‘analytics’ when embedded within the act of meaning-making. That’s why a lot of ‘analytics’ products are so terrible. They assume that analytics is the same as data science with a visualization layer. They don’t acknowledge that analytics only happens when someone ‘makes sense’ out of what is presented.

Analytics is like language. Just like language is not the same as what is represented in the dictionary, analytics is not the same as what is represented in charts and graphs. Sure, words and visualizations are important vehicles for meaning. But just as language goes beyond words (or may not involve words at all), so too does analytics.

It is a mistake to confuse analytics with data science. An it is a mistake to confuse it with visualization. If analytics is about meaning-making, then we are working toward a functional definition rather than a structural one. This shift away from structure to function opens up some really exciting possibilities. For example, SAS is doing some incredible work on the sonic representation of data.

As soon as we begin to think analytics beyond ‘thingness,’ and adopt a more functional definition, its contours dissolve really quickly. If what we are talking about is a rhetorical activity according to which data is rendered meaningful, then we are no longer talking about visualization. We are talking about representation. In a recent talk, I suggested that, to the extend that analytics is detached from a particular mode of representation, and what we are talking about is intentional meaning-making — meaning making intended to solve a particular problem — then a conversation can easily become ‘analytics.’

So analytics is not a ‘thing.’ It is not something that we can point to. Is it an activity? Do we ‘do analytics’? No, analytics isn’t an activity either. Why? Because it is communicative, and so requires the complicity of at least one other. Analytics is not something that we do. It is something we do together. But it is not something that we do together in the same way that we might build a robot together, or watch television together, where what we are talking about is the aggregation of activities. What we are engaged in is something more akin to communication, or love.

Analytics is not a thing. Analytics is not an activity. Analytics is a relation.

As I reached for the gasoline nozzle, I realized at the very last minute that what I thought was regular gasoline was actually ‘plus,’ a grade that I did not want and that I would have paid a premium for. The reason for my near mistake? The way my options were ordered. I expected the grades to be ordered by octane as they almost always are. But in this case, regular 87 was sandwiched between two more premium grades.

The strategy that was employed at the pump at this Shell station in Virginia is an example of ‘nudging.’ It is an example of leveraging preexisting expectations and habits to increase the chances of a particular behavior. There is nothing dishonest about the practice. Information is complete and transparent, and personal freedom to choose is not affected. It is simply that the environment is structured in such a way as to promote one decision instead of others.

Ethically, I like the position of Thaler and Sunstein when they talk about ‘libertarian paternalism.’ In their view, nudging can be a way to reconcile a strong belief in personal freedom with an equally strong belief that certain decisions are better than others. But not all nudges are created equal. Just as it is possible to promote decisions that are better for individuals, so too is it possible to increase the likelihood of choices that serve other interests, and that even serve to subvert the fullest expression of personal liberty, as in the gasoline example above.

One way to think of marketing is as the use of the principles of behavioral economics to change consumer behavior. Marketers are in the business of nudging. Because nudging has a direct impact in human behavior, it is also a fundamentally ethical enterprise. Marketing carries with it a huge burden of responsibility.

What ethical positions do you take in your marketing efforts? What would marketing look like if we were all libertarian paternalists?